CN113781328B - Sigma image filtering method and system - Google Patents
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- CN113781328B CN113781328B CN202110941992.8A CN202110941992A CN113781328B CN 113781328 B CN113781328 B CN 113781328B CN 202110941992 A CN202110941992 A CN 202110941992A CN 113781328 B CN113781328 B CN 113781328B
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Abstract
The invention discloses a sigma image filtering method and system, and belongs to the field of remote sensing image processing. The method comprises the following steps: setting size parameters of a neighborhood window, sequentially filtering each pixel to be detected of the image according to a filtering sequence, constructing a discrimination condition through the statistical characteristics of the neighborhood window, discriminating the pixel to be detected, and dividing the pixel to be detected into normal pixels or abnormal pixels; and constructing screening conditions for abnormal pixel points through statistical characteristics of the neighborhood windows, screening the pixel points in the neighborhood windows corresponding to the abnormal pixel points, calculating corresponding weights by considering the distances from the pixel points to the abnormal pixel points, taking a weighted average value as a correction value of the abnormal pixel points, and finishing filtering of the image. The sigma image filtering method provided by the invention can more completely retain the original real details of the image, has a better inhibition effect on noise which cannot be filtered by the existing sigma filtering method, and further improves the image filtering effect.
Description
Technical Field
The invention belongs to the field of remote sensing image processing, and particularly relates to a sigma image filtering method and system.
Background
In the remote sensing image processing, a plurality of randomly distributed black-and-white spots are found, the brightness value of the black-and-white spots and the surrounding pixel points have great difference, and the abnormal spots often have great noise influence on subsequent scientific researches and are called spot noise. The presence of the speckle noise causes random interference to the inversion of the ground temperature and the soil humidity and the identification of local ground feature characteristics, and influences the inversion or identification precision, so that the method for suppressing the speckle noise is sequentially provided. The average value filtering averages the gray values of the pixel points in the neighborhood window, and the gray values are used as correction values of the pixel points to be detected, so that the speckle noise can be effectively inhibited, but the filtered image is extremely blurred, and the original precision of the image is greatly lost. Based on the method, the sigma filtering method screens the pixel points in the neighborhood window by establishing a range which is 2 times of noise standard deviation from the pixel points to be detected, so that the definition of the image is improved, and compared with mean filtering, the method has a great improvement in the aspect of maintaining the image details. However, the above-mentioned existing sigma filtering method still has the following problems:
(1) The noise standard deviation of the existing sigma filtering method is selected too empirically. The image detail remains more complete when the noise standard deviation is selected to be smaller, but the speckle noise suppression degree is weaker; the better the speckle noise suppression effect is when the noise standard deviation is selected to be larger, but the image becomes more blurred, and the details of the image are easy to lose;
(2) The existing sigma filtering method corrects all pixel points indiscriminately, so that the values of normal pixel points are changed, and the image is distorted;
(3) The influence of geographical factors is not considered in the conventional sigma filtering method when correction value calculation is carried out, and points far away and points close to the average value are considered to have the same influence degree on pixel points to be detected when the average value is calculated.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a sigma image filtering method and a system, and aims to solve the problems that the standard deviation of noise of the sigma filtering method is difficult to select and the image is easy to distort.
In order to achieve the above object, an aspect of the present invention provides a sigma image filtering method, including: setting size parameters of a neighborhood window, sequentially filtering each pixel to be detected of the image according to a filtering sequence, constructing a discrimination condition through the statistical characteristics of the neighborhood window of the pixel to be detected, discriminating the pixel to be detected by using the discrimination condition, and dividing the pixel to be detected into two types of normal pixel and abnormal pixel; when the abnormal pixel points are corrected, screening conditions are constructed through the statistical characteristics of the neighborhood windows of the abnormal pixel points, the screening conditions are used for screening the pixel points in the neighborhood windows corresponding to the abnormal pixel points, the distances from the pixel points to the abnormal pixel points are considered, corresponding weights are calculated, and a weighted average value is taken as a correction value of the abnormal pixel points, so that the filtering process of the image is completed. The filtering order is from left to right from top to bottom.
Preferably, in the sigma filtering method, if a certain sample is to be detectedThe gray value of the pixel point of (a) is a k,l Then the image of the neighborhood window corresponding to the pixel point is square, and the representation form of the image matrix is
Wherein k and l respectively refer to a row number and a column number corresponding to the pixel point to be detected, and if the total number of rows and the total number of columns of the matrix of the image to be filtered are m and n respectively, k is more than or equal to 1 and less than or equal to m, and l is more than or equal to 1 and less than or equal to n; r is a size parameter, satisfiesIt is empirically specified that typically between 2 and 5 min { m, n } represents the minimum of m and n.
Preferably, in the sigma filtering method, the statistical characteristic of the neighborhood window mainly includes a mean valueAnd standard deviation s k,l The calculation formula is as follows
Wherein a is i,j The gray values of the pixel points in the neighborhood window are indicated, and i and j respectively indicate the matrix row number and the column number of the pixel points in the neighborhood window corresponding to the image to be filtered.
Preferably, in the sigma image filtering method, the discrimination conditions constructed by the statistical characteristics of the neighborhood window of the pixel to be detected include: if the gray value of the pixel to be detectedThe pixel point to be detected is a normal pixel point; if->The pixel to be detected is an abnormal pixel.
Preferably, in the sigma image filtering method, when correcting the abnormal pixel, the filtering condition of the statistical characteristic structure of the neighborhood window through the abnormal pixel includes: if the gray value of the pixel point in the neighborhood windowThe pixel point meets the screening condition.
Preferably, in the sigma image filtering method, the correction value of the abnormal pixel is calculated by considering the influence of the distance from the pixel in the neighborhood window to the abnormal pixel, and the formula is as follows
Wherein a is k,l ' is the gray value a of the pixel to be detected k,l Is a correction value of (2); p is p i,j Is a filtered signal value expressed in a formula in the sense that the neighborhood window is built inGray value a of pixel point in range i,j And taking the weighted average value as a correction value of the pixel point to be detected.
According to another aspect of the present invention, there is provided a sigma image filtering system comprising a computer readable storage medium and a processor;
the computer-readable storage medium is for storing executable instructions;
the processor is configured to read executable instructions stored in the computer readable storage medium and execute the sigma image filtering method according to the first aspect of the invention.
In general, the sigma image filtering method provided by the invention only utilizes the pixel points meeting the screening conditions in the neighborhood window of the abnormal pixel points to calculate the corrected value of the gray value of the abnormal pixel points, eliminates the influence of partial noise points on the gray value of the calculated abnormal pixel points, and plays a good role in inhibiting the noise which cannot be filtered by the existing sigma image filtering method on the basis of more completely retaining the original real details of the image.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a sigma image filtering method provided by the present invention;
FIG. 2 is an unfiltered remote sensing image;
FIG. 3 is a remote sensing image of FIG. 2 filtered by the mean filtering method;
FIG. 4 is a remote sensing image of FIG. 2 filtered by a prior art sigma filtering method;
FIG. 5 is a remote sensing image of FIG. 2 after filtering by the sigma filtering method provided by the present invention;
fig. 6 is a neighborhood window corresponding to a pixel to be detected.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
A sigma image filtering method, whose filtering flow diagram is shown in figure 1, assumes that a remote sensing image is composed of m rows and n columns of pixel points, and is marked as A m×n Then pair A m×n The filtering includes the steps of:
(1) And setting a size parameter r of the neighborhood window. The size parameter r satisfiesIt is empirically specified that typically between 2 and 5 min { m, n } represents the minimum of m and n.
(2) And filtering sequentially according to the filtering sequence. And (3) performing filtering operation on each pixel point to be detected in a left-to-right top-to-bottom sequence, wherein the operation steps are as follows:
(2.1) calculating statistical properties of the neighborhood window. The method comprises the following two steps:
(2.1.1) firstly, setting a square neighborhood window of (2r+1) x (2r+1) by taking a pixel point to be detected as a center according to a size parameter r. If the pixel point to be detected is a k,l Then the pixel point a to be detected k,l The image matrix of the corresponding neighborhood window can be expressed as:
denoted as W (2r+1)×(2r+1) . k. l respectively refers to the row number and the column number of the pixel point to be detected corresponding to the image matrix to be filtered (r is more than or equal to k and less than or equal to m-r, and r is more than or equal to l and less than or equal to n-r).
(2.1.2) calculating the mean of the value samples within the neighborhood windowAnd standard deviation s k,l . The calculation formula is as follows
a i,j And the gray values of the pixel points in the neighborhood window are represented, and i and j are respectively the row number and the column number of the matrix of the image to be filtered corresponding to the pixel points in the neighborhood window.
And (2.2) constructing a judging condition through the statistical characteristics of the neighborhood window, and judging whether the pixel point to be detected is abnormal or not. If the pixel point to be detectedThen a k,l Is a normal pixel pointDirectly entering a filtering process of the next pixel without correction; if->Then a k,l For an abnormal pixel point, correction is needed, and the calculation process of the correction value of the abnormal pixel point is as follows:
(2.2.1) constructing screening conditions through the statistical characteristics of the neighborhood window, and screening out pixel points meeting the conditions. The screening condition for the pixel points in the neighborhood window is shown as follows
Wherein p is i,j To screen signal values, when p i,j When=1, this pixel satisfies the screening condition, and the correction value of the abnormal pixel should be calculated.
(2.2.2) weight calculation. Taking the reciprocal of the distance from the pixel point to the abnormal pixel point in the neighborhood window as a weight factor, and the weight calculation formula is as follows:
(2.2.3) mean calculation and correction. And taking the weighted average value of the gray values of the pixel points in the neighborhood window as the correction value of the abnormal pixel point. Abnormal pixel point a k,l Correction value a of (2) k,l The' calculation formula is as follows:
will w i,j The calculation formula is put into order to obtain
And correcting the abnormal pixel point, and then entering a filtering process of the next pixel point.
The sigma image filtering method provided by the invention is illustrated by a 372×445 remote sensing image, and specifically comprises the following steps:
(1) The size parameter r=3 of the neighborhood window is set according to the image quality.
(2) And performing filtering operation on each pixel point to be detected in the order from left to right and from top to bottom. In order to make the description more specific and understandable, the present invention is described by taking a filtering operation of a pixel to be detected as an example, and as shown in fig. 6, a square neighborhood window of (2r+1) × (2r+1), i.e. 7×7, is shown, and the center of the square is the pixel to be detected. The process of filtering the pixel point to be detected comprises the following two steps:
(2.1) calculating statistical properties of the neighborhood window.
And (2.2) constructing a judging condition through the statistical characteristics of the neighborhood window, and judging whether the pixel point to be detected is abnormal or not. Due toTherefore, the pixel to be detected is +.>Then a k,l For an abnormal pixel point, correction is needed, and the calculation process of the correction value of the abnormal pixel point is as follows:
(2.2.1) constructing screening conditions through the statistical characteristics of the neighborhood window, and screening out pixel points meeting the conditions. The screening condition for the pixel points in the neighborhood window is shown as follows
Wherein p is i,j For screening signal values, the calculated screening signal value matrix is as follows:
(2.2.2) weight calculation. Taking the reciprocal of the distance from the pixel point to the abnormal pixel point in the neighborhood window as a weight factor, and the weight calculation formula is as follows:
the weight matrix calculated is as follows:
| 0.0113 | 0.0133 | 0.0152 | 0.0160 | 0.0152 | 0.0000 | 0.0113 |
| 0.0133 | 0.0170 | 0.0215 | 0.0240 | 0.0215 | 0.0170 | 0.0133 |
| 0.0152 | 0.0215 | 0.0339 | 0.0480 | 0.0339 | 0.0215 | 0.0152 |
| 0.0160 | 0.0240 | 0.0480 | 0.0000 | 0.0480 | 0.0240 | 0.0160 |
| 0.0152 | 0.0215 | 0.0339 | 0.0480 | 0.0339 | 0.0215 | 0.0152 |
| 0.0133 | 0.0170 | 0.0215 | 0.0240 | 0.0215 | 0.0170 | 0.0133 |
| 0.0000 | 0.0133 | 0.0152 | 0.0160 | 0.0152 | 0.0133 | 0.0113 |
(2.2.3) mean calculation and correction. And taking the weighted average value of the gray values of the pixel points in the neighborhood window as the correction value of the abnormal pixel point. Abnormal pixel point a k,l Correction value a of (2) k,l ' then:
the filtering operation is performed on fig. 2 according to the implementation flow described above, and the comparison is performed with reference to the effect diagrams of the mean filtering method and the conventional sigma filtering method, as shown in fig. 2, 3, 4 and 5. As can be seen from the image filtering effect graph, the image after mean filtering is blurred, and compared with mean filtering, the conventional sigma filtering is better in image detail. The sigma filtering method of the invention more completely retains the real details of the image, and effectively filters the strong noise points which cannot be filtered by the existing sigma filtering method. Therefore, the sigma filtering method provided by the invention has certain advantages as a remote sensing image filtering method, and can provide certain technical support for the field of remote sensing image processing.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A sigma image filtering method, comprising the steps of:
setting size parameters of a neighborhood window of the pixel to be detected, sequentially selecting each pixel to be detected of the image according to a filtering sequence, constructing a discrimination condition through the statistical characteristics of the neighborhood window, discriminating the pixel to be detected by using the discrimination condition, and dividing the pixel to be detected into a normal pixel or an abnormal pixel; if the gray value of a pixel to be detected is a k,l Then the image of the neighborhood window corresponding to the pixel point is square, and the representation form of the image matrix is
Wherein k and l respectively refer to a row number and a column number corresponding to a pixel point to be detected, k is more than or equal to 1 and less than or equal to m, l is more than or equal to 1 and less than or equal to n, and m and n are respectively the row number and the column number of a matrix of an image to be filtered; r is the size parameter of the neighborhood window, and meets the following requirementsStatistical properties of the neighborhood window include mean +.>And standard deviation s k,l The calculation formula is as follows
Wherein a is i,j The gray values of the pixel points in the neighborhood window are respectively indicated, i and j are respectively indicated to correspond to the image to be filtered of the pixel points in the neighborhood windowMatrix row number and column number;
for the abnormal pixel points, constructing screening conditions through the statistical characteristics of the neighborhood windows of the abnormal pixel points, screening the pixel points in the neighborhood windows corresponding to the abnormal pixel points by using the screening conditions, calculating corresponding weights by considering the distances from the pixel points to the abnormal pixel points, taking a weighted average value as a correction value of the abnormal pixel points, and finishing filtering the image; calculating a correction value of an abnormal pixel point by considering the distance from the pixel point in the neighborhood window to the abnormal pixel point, wherein the formula is as follows:
wherein a is k,l ' is the gray value a of the pixel to be detected k,l Is a correction value of (2); p is p i,j Is a filtering signal value, which indicates that the neighborhood window is built inGray value a of pixel point in range i,j And taking the weighted average value as a correction value of the gray value of the pixel point to be detected.
2. The sigma image filtering method of claim 1, wherein the discrimination conditions are constructed by statistical characteristics of a neighborhood window of pixel points to be detected: if the gray value of the pixel to be detectedThe pixel point to be detected is a normal pixel point; if->The pixel point to be detected is differentAnd (5) a constant pixel point.
3. The sigma image filtering method of claim 1, wherein in the gray value a for the abnormal pixel point k,l When correction is carried out, screening conditions of a statistic characteristic structure of a neighborhood window of the abnormal pixel point are adopted: if the gray value of the pixel point in the neighborhood windowThe pixel points meet the screening conditions; if->The pixel does not meet the screening condition.
4. The sigma image filtering method of claim 1, wherein said filtering order is an order from left to right from top to bottom.
5. A sigma image filtering system comprising a computer readable storage medium and a processor;
the computer-readable storage medium is for storing executable instructions;
the processor is configured to read executable instructions stored in the computer readable storage medium and perform the sigma image filtering method of any of claims 1 to 4.
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| RU2405200C2 (en) * | 2008-07-17 | 2010-11-27 | Корпорация "Самсунг Электроникс Ко., Лтд" | Method and device for fast noise filtration in digital images |
| CN102567973A (en) * | 2012-01-06 | 2012-07-11 | 西安电子科技大学 | Image denoising method based on improved shape self-adaptive window |
| CN104978715A (en) * | 2015-05-11 | 2015-10-14 | 中国科学院光电技术研究所 | Non-local mean image denoising method based on filtering window and parameter self-adaption |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| RU2405200C2 (en) * | 2008-07-17 | 2010-11-27 | Корпорация "Самсунг Электроникс Ко., Лтд" | Method and device for fast noise filtration in digital images |
| CN102567973A (en) * | 2012-01-06 | 2012-07-11 | 西安电子科技大学 | Image denoising method based on improved shape self-adaptive window |
| CN104978715A (en) * | 2015-05-11 | 2015-10-14 | 中国科学院光电技术研究所 | Non-local mean image denoising method based on filtering window and parameter self-adaption |
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